Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7569-2024
https://doi.org/10.5194/gmd-17-7569-2024
Model description paper
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30 Oct 2024
Model description paper | Highlight paper |  | 30 Oct 2024

A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting

Alessandro Maissen, Frank Techel, and Michele Volpi

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Cited articles

Adelson, E., Anderson, C., Bergen, J., Burt, P., and Ogden, J.: Pyramid Methods in Image Processing, RCA Engineer, 29, 33–41, 1984. a
Agou, V. D., Pavlides, A., and Hristopulos, D. T.: Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes, Entropy, 24, 321, https://doi.org/10.3390/e24030321, 2022. a
Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747–2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016. a
Baggi, S. and Schweizer, J.: Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma, Switzerland), Nat. Hazards, 50, 97–108, https://doi.org/10.1007/s11069-008-9322-7, 2009. a
Bellaire, S., Jamieson, J. B., and Fierz, C.: Forcing the snow-cover model SNOWPACK with forecasted weather data, The Cryosphere, 5, 1115–1125, https://doi.org/10.5194/tc-5-1115-2011, 2011. a
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Executive editor
Operational avalanche forecasting has so far been done almost exclusively by human forecasters. For the first time, an automated machine learning approach allows to reach forecasting skills close to human forecasters.
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.